The global electric industry is facing a number of challenges: an aging infrastructure, growing demand, and rapidly changing markets, all of which threaten to reduce the reliability of the electricity supply. Deregulation of the electricity supply industry continues, and the drive to increase efficiencies in power systems have been particularly relevant in the attempt to develop new processes for intelligent observation and management of the grid.
Increasing demand due to economic and demographic variations, without additional generation investments, has led transmission and distribution systems worldwide to their limits of reliable operation. According to the North American Electric Reliability Council (NERC), transmission congestion is expected to continue over the next decade. Growth in demand and the increasing number of energy transactions continue to outstrip the proposed expansion of transmission systems.
Thus, a primary objective of operation and security management in the electric industry is to maximize the infrastructure use while concurrently reducing the risk of system instability and blackouts. To that end, the “optimal power flow” (OPF) problem is utilized to minimize a certain objective over certain power network variables under a set of given constraints. The “objective” may be the minimization of generation cost, or maximization of user utilities. The “variables” typically include real and reactive power outputs, bus voltages and voltage angles. The constraints may be bounds on voltages or power levels, or that the line loading not exceed thermal or stability limits. Due to the nonlinear nature of these variables and constraints, numerical methods are employed to obtain a solution that is within an acceptable tolerance.
Over the years, various algorithmic techniques have been examined to improve the operation speed of the numerical methods applied to the OPF analysis. Several constrained optimization techniques, such as Lagrange multiplier methods, penalty function methods and sequential quadratic programming, coupled with gradient methods and Newton methods for unconstrained optimization, emerged as the leading nonlinear programming (NLP) algorithms for solving AC OPFs. More recently, algorithms based on the primal-dual interior point method have gained popularity. See, for example, U.S. Pat. No. 6,775,597 entitled “Security Constrained Optimal Power Flow Method” issued to P. Ristanovic et al. on Aug. 10, 2004 and assigned to the assignee of this application.
Despite the advancements being made, a full utilization of OPF has not been widely adopted as part of a real-time analysis in large-scale power systems inasmuch as the number of constraints and variables is overwhelming. Instead, system operators often use simplified OPF tools that are based on linear programming (LP) and de-coupled (DC) system models. Historically, this limitation was based on the lack of powerful computer hardware and efficient AC OPF algorithms. With the advent of fast, low-cost computers, however, speed has now become a secondary concern, after algorithm robustness. The remaining prevalent argument for using LP-based DC OPF instead of NLP-based AC OPF is that LP algorithms are deterministic and always yield a solution (albeit not necessarily the desired solution), while NLP algorithms are less robust and often experience convergence problems.
A need remaining in the art, therefore, is for a technique that utilizes the preferred NLP algorithms while overcoming the problems associated with the time-consuming nature of these algorithms.